Intelligent Software Optimization System for Real-Time Application Performance
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Solution Overview
Problem
Current methods for optimizing applications are inefficient and time-consuming, relying on manual analysis and hypothesis formation to improve key performance indicators (KPIs) such as daily active users and monetization rates, often requiring extensive data analyst teams and frequent software updates.
Innovation Solution
The implementation of Intelligent Software Optimization Systems and Methods (ISOSM) that automate analytics and optimization through behavioral analytics, pattern recognition, and predictive analytics, using a state-based event stream model, dynamic data transformation, and user segmentation to identify high-value user profiles and optimize application performance in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis and hypothesis formation methods are used to optimize KPIs, then application performance can be improved, but the process becomes time-consuming and resource-intensive requiring large data analyst teams
Solution Approach 1:
The system enables self-service optimization by automatically performing data analysis, hypothesis generation, and solution implementation without requiring manual intervention from data analyst teams. The automated system processes KPI data, identifies optimization opportunities, and executes changes independently, eliminating the time-consuming manual analysis phase while maintaining optimization effectiveness
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human analysts manually examining data and forming hypotheses, the system uses automated algorithms and machine learning models to perform the same functions, significantly reducing the time required for optimization cycles while eliminating the need for large analyst teams
2Productivity
If frequent software updates are implemented to apply optimization changes, then KPI improvement can be achieved, but development resources and time are consumed
Solution Approach 1:
The system implements dynamic optimization where changes are applied in real-time based on current performance data rather than through fixed release cycles. The system continuously monitors KPIs and automatically adjusts parameters, allowing optimization to occur dynamically without requiring frequent software updates or version releases, thus reducing development resource consumption
3Loss of information
If manual data analysis by analyst teams is used, then KPI insights can be discovered, but resource consumption and operational costs increase
Solution Approach 1:
The automated system performs data analysis independently without requiring human analyst teams. It automatically processes raw data, identifies patterns, generates hypotheses, and derives insights regarding KPI optimization. This self-service capability maintains high-quality insight generation while eliminating the need for numerous data analysts, thereby reducing operational costs and resource consumption
Data Source
AI summary
Systems and methods are disclosed for optimizing applications per user. In one exemplary implementation, there is provided a method for optimizing an application by monitoring performance indicators of the application. Users are classified based on the performance indicators into sets. Behavior patterns are identified among user sets. Moreover, illustrative methods may include modifying configurable tuning variables of the application based on the behavior patterns.